Method and apparatus for signal interference processing

ABSTRACT

A system that incorporates the subject disclosure may include, for example, a device comprising a memory to store instructions and a processor coupled to the memory, wherein responsive to executing the instructions, the processor performs operations. The operations comprise receiving signals over a spectrum of frequencies, providing location data of the device to a base station, receiving a request from a base station to perform a spectral analysis of the signals, detecting an interference among the signals, and providing, in response to the request, data to the base station regarding a source of the interference, wherein the data comprises a location of the source relative to the device, spectral data for identifying the source, and a time a frequency of occurrence of the interference. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/961,515 filed Aug. 7, 2013 by Abdelmonem et al., entitled “Method andApparatus for Signal Interference Processing which claims the benefit ofpriority to U.S. Provisional Application No. 61/792,184 filed on Mar.15, 2013, entitled, “Method and apparatus for Interference Detection andMitigation,” the disclosure of all of which are hereby incorporatedherein by reference in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure is related to a method and apparatus for signalinterference processing.

BACKGROUND

In most communication environments involving short range or long rangewireless communications, interference from unexpected wireless sourcescan impact the performance of a communication system leading to lowerthroughput, dropped calls, reduced bandwidth which can cause trafficcongestion, or other adverse effects, which are undesirable.

Some service providers of wireless communication systems have addressedinterference issues by adding more communication nodes, policinginterferers, or utilizing antenna steering techniques to avoidinterferers.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 depicts an illustrative embodiment of a communication system;

FIG. 2 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal;

FIG. 3 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal showing unequal power balancing between thefour CDMA carriers and including a narrowband interferer;

FIG. 4 depicts an illustrative embodiment of a base station of FIG. 1;

FIG. 5 depicts an illustrative embodiment of a frequency spectrum of afour carrier CDMA signal having four CDMA carriers with suppression of anarrowband interferer that results in falsing;

FIG. 6 depicts an illustrative embodiment of an interference detectionand mitigation system;

FIG. 7 depicts an illustrative embodiment of an interference detectionand mitigation system;

FIG. 8 depicts an illustrative embodiment of signal processing module ofFIG. 7;

FIG. 9 depicts an illustrative embodiment of plots of a spread spectrumsignal;

FIG. 10 depicts an illustrative embodiment of a method for interferencedetection;

FIG. 11 depicts illustrative embodiments of the method of FIG. 10;

FIG. 12 depicts illustrative embodiments of a series of spread spectrumsignals intermixed with an interference signal;

FIG. 13 depicts an illustrative embodiment of a graph depictinginterference detection efficiency of a system of the subject disclosure;

FIG. 14 depicts illustrative embodiments of Long Term Evolution (LTE)time and frequency signal plots;

FIG. 15 depicts illustrative embodiments of LIE time and frequencysignal plots intermixed with interference signals;

FIG. 16 depicts an illustrative embodiment of a method for detecting andmitigating interference signals shown in FIG. 15;

FIG. 17 depicts an illustrative embodiment of adaptive thresholds usedfor detecting and mitigating interference signals shown in FIG. 15;

FIG. 18 depicts an illustrative embodiment of resulting LTE signalsafter mitigating interference according to the method of FIG. 16;

FIG. 19A depicts an illustrative embodiment of a system for detecting,identifying and locating sources of interference;

FIG. 19B depicts an illustrative embodiment of a method for detecting,identifying and locating sources of interference, using the system ofFIG. 19A;

FIG. 20 depicts an illustrative embodiment of a communication devicethat can utilize in whole or in part embodiments of the subjectdisclosure for detecting and mitigating interference; and

FIG. 21 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed, maycause the machine to perform any one or more of the methods describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for detecting and mitigating interference signals. Otherembodiments are included in the subject disclosure.

One embodiment of the subject disclosure includes a device comprising amemory to store instructions and a processor coupled to the memory,wherein responsive to executing the instructions, the processor performsoperations. The operations comprise receiving signals over a spectrum offrequencies, providing location data of the device to a base station,receiving a request from a base station to perform a spectral analysisof the signals, detecting an interference among the signals, andproviding, in response to the request, data to the base stationregarding a source of the interference, wherein the data comprises alocation of the source relative to the device, spectral data foridentifying the source, a time of occurrence of the interference, and afrequency of occurrence of the interference. The data comprises alocation of the source relative to the device, spectral data foridentifying the source, a time of occurrence of the interference, and afrequency of occurrence of the interference.

One embodiment of the subject disclosure includes a base stationcomprising a memory to store instructions and a processor coupled to thememory, wherein responsive to executing the instructions, the processorperforms operations. The operations comprise monitoring a location of amobile device in communication with the base station, sending a requestto the mobile device to perform a spectral analysis of signals detectedby the mobile device, receiving data, provided by the mobile device inresponse to the request, regarding an interference detected among thesignals, wherein the data comprises a location of a source of theinterference relative to the device, spectral data for identifying thesource, a time of occurrence of the interference, and a frequency ofoccurrence of the interference, accessing a database of interferencesources including spectral profiles, identifying the source of theinterference based on a comparison of the spectral data and the spectralprofiles, and generating a report including information regarding theinterference, wherein the report is provided to a diagnostic system tomitigate effects of the interference.

One embodiment of the subject disclosure includes a method, comprisingmonitoring, by a base station comprising a processor, a location of aplurality of mobile devices in communication with the base station;selecting, by the base station, a set of mobile devices from theplurality of mobile devices in accordance with the locations of themobile devices; sending, by the base station, a request to the set ofselected mobile devices to perform a spectral analysis of signalsdetected by the set of mobile devices; receiving, by the base station,data provided by the set of mobile devices in response to the requestand in accordance with the spectral analysis performed by the set ofmobile devices, the data regarding an interference detected among thesignals, wherein the data comprises location data for a source of theinterference relative to each of the set of mobile devices, spectraldata for identifying the source, a time of occurrence of theinterference, and a frequency of occurrence of the interference;determining, by the base station, the location of the source of theinterference from the location data in a triangulation procedure;accessing, by the base station, a database of interference sourcesincluding spectral profiles; identifying, by the base station, thesource of the interference based on a comparison of the spectral dataand the spectral profiles; and generating, by the base station, a reportincluding information regarding the interference, wherein the report isprovided to a diagnostic system to mitigate effects of the interference.

As shown in FIG. 1, an exemplary telecommunication system 10 may includemobile units 12, 13A, 13B, 13C, and 13D, a number of base stations, twoof which are shown in FIG. 1 at reference numerals 14 and 16, and aswitching station 18 to which each of the base stations 14, 16 may beinterfaced. The base stations 14, 16 and the switching station 18 may becollectively referred to as network infrastructure.

During operation, the mobile units 12, 13A, 13B, 13C, and 13D exchangevoice, data or other information with one of the base stations 14, 16,each of which is connected to a conventional land line communicationnetwork. For example, information, such as voice information,transferred from the mobile unit 12 to one of the base stations 14, 16is coupled from the base station to the communication network to therebyconnect the mobile unit 12 with, for example, a land line telephone sothat the land line telephone may receive the voice information.Conversely, information, such as voice information may be transferredfrom a land line communication network to one of the base stations 14,16, which in turn transfers the information to the mobile unit 12.

The mobile units 12, 13A, 13B, 13C, and 13D and the base stations 14, 16may exchange information in either narrow band or wide band format. Forthe purposes of this description, it is assumed that the mobile unit 12is a narrowband unit and that the mobile units 13A, 13B, 13C, and 13Dare wideband units. Additionally, it is assumed that the base station 14is a narrowband base station that communicates with the mobile unit 12and that the base station 16 is a wideband digital base station thatcommunicates with the mobile units 13A, 13B, 13C, and 13D.

Narrow band format communication takes place using, for example,narrowband 200 kilohertz (kHz) channels. The Global system for mobilephone systems (GSM) is one example of a narrow band communication systemin which the mobile unit 12 communicates with the base station 14 usingnarrowband channels. Alternatively, the mobile units 13A, 13B, 13C, and13D communicate with the base stations 16 using a form of digitalcommunications such as, for example, code-division multiple access(CDMA), Universal Mobile Telecommunications System (UMTS), 3GPP LongTerm Evolution (LTE), or other next generation wireless accesstechnologies. CDMA digital communication, for instance, takes placeusing spread spectrum techniques that broadcast signals having widebandwidths, such as, for example, 1.2288 megahertz (MHz) bandwidths.

The switching station 18 is generally responsible for coordinating theactivities of the base stations 14, 16 to ensure that the mobile units12, 13A, 13B, 13C, and 13D are constantly in communication with the basestation 14, 16 or with some other base stations that are geographicallydispersed. For example, the switching station 18 may coordinatecommunication handoffs of the mobile unit 12 between the base stations14 and another base station as the mobile unit 12 roams betweengeographical areas that are covered by the two base stations.

One particular problem that may arise in the telecommunication system 10is when the mobile unit 12 or the base station 14, each of whichcommunicates using narrowband channels, interferes with the ability ofthe base station 16 to receive and process wideband digital signals fromthe digital mobile units 13A, 13B, 13C, and 13D. In such a situation,the narrowband signal transmitted from the mobile unit 12 or the basestation 14 may interfere with the ability of the base station 16 toproperly receive wideband communication signals.

As will be readily appreciated, the base station 16 may receive andprocess wideband digital signals from more than one of the digitalmobile units 13A, 13B, 13C, and 13D. For example, the base station 16may be adapted to receive and process four CDMA carriers 40A-40D thatfall within a multi-carrier CDMA signal 40, as shown in FIG. 2. In sucha situation, narrowband signals transmitted from more than one mobileunits, such as, the mobile unit 12, may interfere with the ability ofthe base station 16 to properly receive wideband communication signalson any of the four CDMA carriers 40A-40D. For example, FIG. 3 shows amulti-carrier CDMA signal 42 containing four CDMA carriers 42A, 42B, 42Cand 42D adjacent to each other wherein one of the CDMA carriers 42C hasa narrowband interferer 46 therein. As shown in FIG. 3, it is quiteoften the case that the signal strengths of the CDMA carrier signals42A-42D are not equal.

As disclosed in detail hereinafter, a system and/or a method formultiple channel adaptive filtering or interference suppression may beused in a communication system. In particular, such a system or methodmay be employed in a wideband communication system to protect against,or to report the presence of, narrowband interference, which hasdeleterious effects on the performance of the wideband communicationsystem. Additionally, such a system and method may be operated toeliminate interference in CDMA carriers having other CDMA carriersadjacent thereto.

As shown in FIG. 4, the signal reception path of the base station 16,which was described as receiving narrowband interference from the mobileunit 12 in conjunction with FIG. 1, includes an antenna 50 that providessignals to a low noise amplifier (LNA) 52. The output of the LNA 52 iscoupled to a splitter 54 that splits the signal from the LNA 52 into anumber of different paths, one of which may be coupled to an adaptivefront end 56 and another of which may be coupled to a narrowbandreceiver 58. The output of the adaptive front end 56 is coupled to awideband receiver 60, which may, for example, be embodied in a CDMAreceiver or any other suitable wideband receiver. The narrowbandreceiver 58 may be embodied in a 15 kHz bandwidth receiver or in anyother suitable narrowband receiver. Although only one signal path isshown in FIG. 4, it will be readily understood to those having ordinaryskill in the art that such a signal path is merely exemplary and that,in reality, a base station may include two or more such signal pathsthat may be used to process main and diversity signals received by thebase station 16.

It will be readily understood that the illustrations of FIG. 4 can alsobe used to describe the components and functions of other forms ofcommunication devices such as a small base station, a femto cell, a WiFirouter or access point, a cellular phone, a smart phone, a laptopcomputer, a tablet, or other forms of wireless communication devicessuitable for applying the principles of the subject disclosure.Accordingly, such communication devices can include variants of thecomponents shown in FIG. 4 and perform the functions that will bedescribed below. For illustration purposes only, the descriptions belowwill address the base station 16 with an understanding that theseembodiments are exemplary and non-limiting to the subject disclosure.

Referring back to FIG. 4, the outputs of the narrowband receiver 58 andthe wideband receiver 60 can be coupled to other systems within the basestation 16. Such systems may perform voice and/or data processing, callprocessing or any other desired function. Additionally, the adaptivefront end module 56 may also be communicatively coupled, via theInternet, telephone lines, cellular network, or any other suitablecommunication systems, to a reporting and control facility that isremote from the base station 16. In some networks, the reporting andcontrol facility may be integrated with the switching station 18. Thenarrowband receiver 58 may be communicatively coupled to the switchingstation 18 and may respond to commands that the switching station 18issues.

Each of the components 50-60 of the base station 16 shown in FIG. 4,except for the adaptive front end module 56, may be found in aconventional wideband cellular base station 16, the details of which arewell known to those having ordinary skill in the art. It will also beappreciated by those having ordinary skill in the art that FIG. 4 doesnot disclose every system or subsystem of the base station 16 and,rather, focuses on the relevant systems and subsystems to the subjectdisclosure. In particular, it will be readily appreciated that, whilenot shown in FIG. 4, the base station 16 can include a transmissionsystem or other subsystems. It is further appreciated that the adaptivefront end module 56 can be an integral subsystem of a wideband cellularbase station 16, or can be a modular subsystem that can be physicallyplaced in different locations of a receiver chain of the base station16, such as at or near the antenna 50, at or near the LNA 52, or at ornear the wideband receiver 60.

During operation of the base station 16, the antenna 50 receives CDMAcarrier signals that are broadcast from the mobile unit 13A, 13B, 13Cand 13D and couples such signals to the LNA 52, which amplifies thereceived signals and couples the amplified signals to the splitter 54.The splitter 54 splits the amplified signal from the LNA 52 andessentially places copies of the amplified signal on each of its outputlines. The adaptive front end module 56 receives the signal from thesplitter 54 and, if necessary, filters the CDMA carrier signal to removeany undesired narrowband interference and couples the filtered CDMAcarrier signal to the wideband receiver 60.

As noted previously, FIG. 2 illustrates an ideal frequency spectrum 40of a CDMA carrier signal that may be received at the antenna 50,amplified and split by the LNA 52 and the splitter 54 and coupled to theadaptive front end module 56. If the CDMA carrier signal received at theantenna 50 has a frequency spectrum 40 as shown in FIG. 2 without anynarrowband interference, the adaptive front end will not filter the CDMAcarrier signal and will simply couple the wideband signal directlythrough the adaptive front end module 56 to the wideband receiver 60.

However, as noted previously, it is possible that the CDMA carriersignal transmitted by the mobile units 13A-13D and received by theantenna 50 has a frequency spectrum as shown in FIG. 3 which contains amulti-carrier CDMA signal 42 that includes not only the four CDMAcarriers 42A, 42B, 42C and 42D from the mobile units 13A, 13B, 13C and13D having unequal CDMA carrier strengths, but also includes narrowbandinterferer 46, as shown in FIG. 3, which in this illustration is causedby mobile unit 12. If a multi-carrier CDMA signal having a multi-carrierCDMA signal 42 including narrowband interferer 46 is received by theantenna 50 and amplified, split and presented to the adaptive front endmodule 56, it will filter the multi-carrier CDMA signal 42 to produce afiltered frequency spectrum 43 as shown in FIG. 5.

The filtered multi-carrier CDMA signal 43 has the narrowband interferer46 removed, as shown by the notch 46A. The filtered multi-carrier CDMAsignal 43 is then coupled from the adaptive front end module 56 to thewideband receiver 60, so that the filtered multi-carrier CDMA signal 43may be demodulated. Although some of the multi-carrier CDMA signal 42was removed during filtering by the adaptive front end module 56,sufficient multi-carrier CDMA signal 43 remains to enable the widebandreceiver 60 to recover the information that was broadcast by mobileunit(s). Accordingly, in general terms, the adaptive front end module 56selectively filters multi-carrier CDMA signals to remove narrowbandinterference therefrom. Further detail regarding the adaptive front endmodule 56 and its operation is provided below in conjunction with FIGS.6-20.

FIG. 3 depicts another example embodiment of the adaptive front endmodule 56. As noted earlier, the adaptive front end module 56 can beutilized by any communication device including cellular phones,smartphones, tablets, small base stations, femto cells, WiFi accesspoints, and so on. In the illustration of FIG. 3, the adaptive front endmodule 56 can include a radio 60 comprising two stages, a receiver stage62 and a transmitter stage 64, each coupled to an antenna assembly 66,66′, which may comprise one of more antennas for the radio 60. The radio60 has a first receiver stage coupled to the antenna assembly 66 andincludes an adaptive front-end controller 68 that receives the input RFsignal from the antenna and performs adaptive signal processing on thatRF signal before providing the modified RF signal to ananalog-to-digital converter 70, which then passes the adapted RF signalto a digital RF tuner 72.

As shown in FIG. 6, the adaptive front end controller 68 of the receiverstage 62 includes two RF signal samplers 74, 76 connected between an RFadaptive filter stage 78 that is controlled by controller 80. Theadaptive filter stage 78 may have a plurality of tunable digital filtersthat can sample an incoming signal and selectively provide bandpass orbandstop signal shaping of an incoming RF signal, whether it is anentire wideband communication signal or a narrowband signal or variouscombinations of both. A controller 80 is coupled to the samplers 74, 76and filter stage 78 and serves as an RF link adapter that along with thesampler 74 monitors the input RF signal from the antenna 66 anddetermines various RF signal characteristics such as the interferencesand noise within the RF signal. The controller 80 is configured toexecute any number of a variety of signal processing algorithms toanalyze the received RF signal, and determine a filter state for thefilter stage 78.

By providing tuning coefficient data to the filter stage 78, theadaptive front end controller 68 acts to pre-filter the received RFsignal before the signal is sent to the RF tuner 72, which analyzes thefiltered RF signal for integrity and/or for other applications such ascognitive radio applications. After filtering, the radio tuner 72 maythen perform channel demodulation, data analysis, and local broadcastingfunctions. The RF tuner 72 may be considered the receiver side of anoverall radio tuner, while RF tuner 72′ may be considered thetransmitter side of the same radio tuner. Prior to sending the filteredRF signal, the sampler 76 may provide an indication of the filtered RFsignal to the controller 80 in a feedback manner for further adjustingof the adaptive filter stage 78.

In some examples, the adaptive front-end controller 68 is synchronizedwith the RF tuner 72 by sharing a master clock signal communicatedbetween the two. For example, cognitive radios operating on a 100 μsresponse time can be synchronized such that for every clock cycle theadaptive front end analyzes the input RF signal, determines an optimalconfiguration for the adaptive filter stage 78, filters that RF signalinto the filtered RF signal and communicates the same to the radio tuner72 for cognitive analysis at the radio. By way of example, cellularphones may be implemented with a 200 μs response time on filtering. Byimplementing the adaptive front end controller 68 using a fieldprogrammable gate array configuration for the filter stage, wirelessdevices may identify not only stationary interference, but alsonon-stationary interference, of arbitrary bandwidths on that movinginterferer.

In some implementations, the adaptive front-end controller 68 may filterinterference or noise from the received incoming RF signal and pass thatfiltered RF signal to the tuner 72. In other examples, such as cascadedconfigurations in which there are multiple adaptive filter stages, theadaptive front-end controller 68 may be configured to apply the filteredsignal to an adaptive bandpass filter stage to create a passband portionof the filtered RF signal. For example, the radio tuner 72 maycommunicate information to the controller 68 to instruct the controllerthat the radio is only looking at a portion of an overall RF spectrumand thus cause the adaptive front-end controller 68 not to filtercertain portions of the RF spectrum and thereby bandpass only thoseportions. The integration between the radio tuner 72 and the adaptivefront-end controller 68 may be particularly useful in dual-band andtri-band applications in which the radio tuner 72 is able to communicateover different wireless standards, such as GSM or UMTS standards.

The algorithms that may be executed by the controller 80 are not limitedto interference detection and filtering of interference signals. In someconfigurations the controller 80 may execute a spectral blind sourceseparation algorithm that looks to isolate two sources from theirconvolved mixtures. The controller 80 may execute a signal tointerference noise ratio (SINR) output estimator for all or portions ofthe RF signal. The controller 80 may perform bidirectional transceiverdata link operations for collaborative retuning of the adaptive filterstage 78 in response to instructions from the radio tuner 72 or fromdata the transmitter stage 64. The controller 80 can determine filtertuning coefficient data for configuring the various adaptive filters ofstage 78 to properly filter the RF signal. The controller 80 may alsoinclude a data interface communicating the tuning coefficient data tothe radio tuner 72 to enable the radio tuner 72 to determine filteringcharacteristics of the adaptive filter 78.

In one embodiment the filtered RF signal may be converted from a digitalsignal to an analog signal within the adaptive front-end controller 68.This allows the controller 68 to integrate in a similar manner toconventional RF filters. In other examples, a digital interface may beused to connect the adaptive front-end controller 68 with the radiotuner 72, in which case the ADC 70 would not be necessary.

The above discussion is in the context of the receiver stage 62. Similarelements are shown in the transmitter stage 64, but bearing a prime. Theelements in the transmitter stage 64 may be similar to those of thereceiver 62, with the exception of the digital to analog converter (DAC)70′ and other adaptations to the other components shown with a prime inthe reference numbers. Furthermore, some or all of these components mayin fact be executed by the same corresponding structure in the receiverstage 62. For example, the RF receiver tuner 72 and the transmittertuner 72′ may be performed by a single tuner device. The same may betrue for the other elements, such as the adaptive filter stages 78 and78′, which may both be implemented in a single FPGA, with differentfilter elements in parallel for full duplex (simultaneous) receive andtransmit operation.

FIG. 7 illustrates another example implementation of an adaptivefront-end controller 100. Input RF signals are received at an antenna(not shown) and coupled to an initial analog filter 104, such as lownoise amplifier (LNA) block, then digitally converted via an analog todigital converter (ADC) 106, prior to the digitized input RF signalbeing coupled to a field programmable gate array (FPGA) 108. Theadaptive filter stage described above may be implemented within the FPGA108, which has been programmed to contain a plurality of adaptive filterelements tunable to different operating frequencies and frequency bands,and at least some being adaptive from a bandpass to a bandstopconfiguration or vice versa, as desired. Although an FPGA isillustrated, it will be readily understood that other architectures suchas an application specific integrated circuit (ASIC) or a digital signalprocessor (DSP) may also be used to implement a digital filterarchitecture described in greater detail below.

A DSP 110 is coupled to the FPGA 108 and executes signal processingalgorithms that may include a spectral blind source separationalgorithm, a signal to interference noise ratio (SINR) output estimator,bidirectional transceiver data line operation for collaborative retuningof the adaptive filter stage in response to instructions from the tuner,and/or an optimal filter tuning coefficients algorithm.

FPGA 108 is also coupled to a PCI target 112 that interfaces the FPGA108 and a PCI bus 114 for communicating data externally. A system clock118 provides a clock input to the FPGA 108 and DSP 110, therebysynchronizing the components. The system clock 118 may be locally set onthe adaptive front-end controller, while in other examples the systemclaim 118 may reflect an external master clock, such as that of a radiotuner. The FPGA 108, DSP 110, and PCI target 112, designatedcollectively as signal processing module 116, will be described ingreater detail below. In the illustrated example, the adaptive front-endcontroller 100 includes a microcontroller 120 coupled to the PCI bus 114and an operations, alarms and metrics (OA&M) processor 122. Althoughthey are shown and described herein as separate devices that executeseparate software instructions, those having ordinary skill in the artwill readily appreciate that the functionality of the microcontroller120 and the OA&M processor 122 may be merged into a single processingdevice. The microcontroller 120 and the OA&M processor 122 are coupledto external memories 124 and 126, respectively. The microcontroller 120may include the ability to communicate with peripheral devices, and, assuch, the microcontroller 120 may be coupled to a USB port, an Ethernetport, or an RS232 port, among others (though none shown). In operation,the microcontroller 120 may locally store lists of channels havinginterferers or a list of known typically available frequency spectrumbands, as well as various other parameters. Such a list may betransferred to a reporting and control facility or a base station, viathe OA&M processor 122, and may be used for system diagnostic purposes.

The aforementioned diagnostic purposes may include, but are not limitedto, controlling the adaptive front-end controller 100 to obtainparticular information relating to an interferer and retasking theinterferer. For example, the reporting and control facility may use theadaptive front-end controller 100 to determine the identity of aninterferer, such as a mobile unit, by intercepting the electronic serialnumber (ESN) of the mobile unit, which is sent when the mobile unittransmits information on the narrowband channel. Knowing the identity ofthe interferer, the reporting and control facility may contactinfrastructure that is communicating with the mobile unit (e.g., thebase station) and may request the infrastructure to change the transmitfrequency for the mobile unit (i.e., the frequency of the narrowbandchannel on which the mobile unit is transmitting) or may request theinfrastructure to drop communications with the interfering mobile unitaltogether.

Additionally, in a cellular configuration (e.g., a system based on aconfiguration like that of FIG. 1) diagnostic purposes may include usingthe adaptive front-end controller 100 to determine a telephone numberthat the mobile unit is attempting to contact and, optionally handlingthe call. For example, the reporting and control facility may use theadaptive front-end controller 100 to determine that the user of themobile unit was dialing 911, or any other emergency number, and may,therefore, decide that the adaptive front-end controller 100 should beused to handle the emergency call by routing the output of the adaptivefront-end controller 100 to a telephone network.

The FPGA 108 can provide a digital output coupled to a digital to analogconverter (DAC) 128 that converts the digital signal to an analog signalwhich may be provided to a filter 130 to generate a filtered RF outputto be broadcast from the base station or mobile station. The digitaloutput at the FPGA 108, as described, may be one of many possibleoutputs. For example, the FPGA 108 may be configured to output signalsbased on a predefined protocol such as a Gigabit Ethernet output, anopen base station architecture initiative (OBSAI) protocol, or a commonpublic radio interface (CPRI) protocol, among others.

It is further noted that the aforementioned diagnostic purposes may alsoinclude creating a database of known interferers, the time of occurrenceof the interferers, the frequency of occurrence of the interferers,spectral information relating to the interferers, a severity analysis ofthe interferers, and so on. The identity of the interferers may be basedsolely on spectral profiles of each interferer that can be used foridentification purposes. Although the aforementioned illustrationsdescribe a mobile unit 12 as an interferer, other sources ofinterference are possible. Any electronic appliance that generateselectromagnetic waves such as, for example, a computer, a set-top box, achild monitor, a wireless access point (e.g., WiFi, ZigBee, Bluetooth,etc.) can be a source of interference. In one embodiment, a database ofelectronic appliances can be analyzed in a laboratory setting or othersuitable testing environment to determine an interference profile foreach appliance. The interference profiles can be stored in a databaseaccording to an appliance type, manufacturer, model number, and otherparameters that may be useful in identifying an interferer. Spectralprofiles provided by, for example, the OA&M processor 108 to adiagnostic system can be compared to a database of previouslycharacterized interferers to determine the identity of the interferencewhen a match is detected.

A diagnostic system, whether operating locally at the adaptive front endcontroller, or remotely at a base station, switching station, or serversystem, can determine the location of the interferer near the basestation (or mobile unit) making the detection, or if a more preciselocation is required, the diagnostic system can instruct several basestations (or mobile units) to perform triangulation analysis to moreprecisely locate the source of the interference if the interference isfrequent and measureable from several vantage points. With locationdata, interference identity, timing and frequency of occurrence, thediagnostic system can generate temporal and geographic reports showinginterferers providing field personnel a means to assess the volume ofinterference, its impact on network performance, and it may providesufficient information to mitigate interference by means other thanfiltering, such as, for example, interference avoidance by way ofantenna steering at the base station, beam steering, retasking aninterferer when possible, and so on.

FIG. 8 illustrates further details of an example implementation of asignal processing module 116 that may serve as another embodiment of anadaptive front end controller, it being understood that otherarchitectures may be used to implement a signal detection algorithm. Adecoder 150 receives an input from the ADC 106 and decodes the incomingdata into a format suitable to be processed by the signal processingmodule 116. A digital down converter 152, such as a polyphase decimator,down converts the decoded signal from the decoder 150. The decodedsignal is separated during the digital down conversion stage into acomplex representation of the input signal, that is, into In-Phase (I)and Quadrature-Phase (Q) components which are then fed into a tunableinfinite impulse response (IIR)/finite impulse response (FIR) filter154. The IIR/FIR filter 154 may be implemented as multiple cascaded orparallel IIR and FIR filters. For example, the IIR/FIR filter 154 may beused with multiple filters in series, such as initial adaptive bandpassfilter followed by adaptive bandstop filter. For example, the bandpassfilters may be implemented as FIR filters, while the bandstop filtersmay be implemented as IIR filters. In an embodiment, fifteen cascadedtunable IIR/FIR filters are used to optimize the bit width of eachfilter. Of course other digital down converters and filters such ascascaded integrator-comb (CIC) filters may be used, to name a few. Byusing complex filtering techniques, such as the technique describedherein, the sampling rate is lowered thereby increasing (e.g., doubling)the bandwidth that the filter 154 can handle. In addition, using complexarithmetic also provides the signal processing module 116 the ability toperform higher orders of filtering with greater accuracy.

The I and Q components from the digital down converter 152 are providedto the DSP 110 which implements a detection algorithm and in responseprovides the tunable IIR/FIR filter 154 with tuning coefficient datathat tunes the IIR and/or FIR filters 154 to specific notch (orbandstop) and/or bandpass frequencies, respectively, and specificbandwidths. The tuning coefficient data, for example, may include afrequency and a bandwidth coefficient pair for each of the adaptivefilters, which enables the filter to tune to a frequency for bandpass orbandstop operation and the bandwidth to be applied for that operation.The tuning coefficient data corresponding to a bandpass center frequencyand bandwidth may be generated by the detection algorithm and passed toa tunable FIR filter within the IIR/FIR filter 154. The filter 154 maythen pass all signals located within a passband of the giventransmission frequency. Tuning coefficient data corresponding to a notch(or bandstop) filter may be generated by the detection algorithm andthen applied to an IIR filter within the IIR/FIR filter 154 to removeany narrowband interference located within the passband of the bandpassfilter. The tuning coefficient data generated by the detection algorithmare implemented by the tunable IIR/FIR filters 154 using mathematicaltechniques known in the art. In the case of a cognitive radio, uponimplementation of the detection algorithm, the DSP 110 may determine andreturn coefficients corresponding to a specific frequency and bandwidthto be implemented by the tunable IIR/FIR filter 154 through a DSP/PCIinterface 158. Similarly, the transfer function of a notch (or bandstop)filter may also be implemented by the tunable IIR/FIR filter 154. Ofcourse other mathematical equations may be used to tune the IIR/FIRfilters 154 to specific notch, bandstop, or bandpass frequencies and toa specific bandwidth.

After the I and Q components are filtered to the appropriate notch (orbandstop) or bandpass frequency at a given bandwidth, a digitalupconverter 156, such as a polyphase interpolator, converts the signalback to the original data rate, and the output of the digitalupconverter is provided to the DAC 128.

A wireless communication device capable to be operated as a dual- ortri-band device communicating over multiple standards, such as over GSMand UMTS may use the adaptive digital filter architecture embodiments asdescribed above. For example, a dual-band device (using both UMTS andGSM) may be preprogrammed within the DSP 110 to transmit first on UMTS,if available, and on GSM only when outside of a UMTS network. In such acase, the IIR/FIR filter 154 may receive tuning coefficient data fromthe DSP 110 to pass all signals within a UMTS range. That is, the tuningcoefficient data may correspond to a bandpass center frequency andbandwidth adapted to pass only signals within the UMTS range. Thesignals corresponding to a GSM signal may be filtered, and anyinterference caused by the GSM signal may be filtered using tuningcoefficients, received from the DSP 110, corresponding to a notch (orbandstop) frequency and bandwidth associated with the GSM interferencesignal.

Alternatively, in some cases it may be desirable to keep the GSM signalin case the UMTS signal fades quickly and the wireless communicationdevice may need to switch communication standards rapidly. In such acase, the GSM signal may be separated from the UMTS signal, and bothpassed by the adaptive front-end controller. Using the adaptive digitalfilter, two outputs may be realized, one output corresponding to theUMTS signal and one output corresponding to a GSM signal. The DSP 110may be programmed to again recognize the multiple standard service andmay generate tuning coefficients corresponding to realize a filter, suchas a notch (or bandstop) filter, to separate the UMTS signal from theGSM signal. In such examples, an FPGA may be programmed to have paralleladaptive filter stages, one for each communication band.

To implement the adaptive filter stages, in some examples, the signalprocessing module 116 is pre-programmed with general filter architecturecode at the time of production, for example, with parameters definingvarious filter types and operation. The adaptive filter stages may thenbe programmed, through a user interface or other means, by the serviceproviders, device manufactures, etc., to form the actual filterarchitecture (parallel filter stages, cascaded filter stages, etc.) forthe particular device and for the particular network(s) under which thedevice is to be used. Dynamic flexibility can be achieved duringruntime, where the filters may be programmed to different frequenciesand bandwidths, each cycle, as discussed herein.

One method of detecting a wideband signal having narrowband interferenceis by exploiting the noise like characteristics of a signal. Due to suchnoise like characteristics of the signal, a particular measurement of anarrowband channel power gives no predictive power as to what the nextmeasurement of the same measurement channel may be. In other words,consecutive observations of power in a given narrowband channel areun-correlated. As a result, if a given measurement of power in anarrowband channel provides predictive power over subsequentmeasurements of power in that particular channel, thus indicating adeparture from statistics expected of a narrowband channel withoutinterference, such a narrowband channel may be determined to containinterference.

FIG. 9 illustrates an IS-95 CDMA signal 202, which is a generic DirectSequence Spread Spectrum (DSSS) signal. The CDMA signal 202 may have abandwidth of 1.2288 MHz and it may be used to carry up to 41 narrowbandchannels, each of which has a bandwidth of 30 kHz. One way to identifyinterference affecting the CDMA signal 202 may be to identify any ofsuch 41 narrowband channels having excess power above an expected powerof the CDMA signal 202. FIG. 9 also illustrates the probabilitydistribution functions (PDFs) 204 of a typical DSSS signal and acomplementary cumulative distribution functions (CCDFs) 206 of a typicalDSSS signal, which may be used to establish a criteria used to determinenarrowband channels disposed within a wideband signal and having excesspower.

Specifically, the PDFs 204 include probability distribution of power ina given channel, which is the likelihood p(x) of measuring a power x ina given channel, for a DSSS signal carrying one mobile unit (212), for aDSSS signal carrying ten mobile units (214), and for a DSSS signalcarrying twenty mobile units (210). For example, for the PDF 212,representing a DSSS signal carrying one mobile unit, the distributionp(x) is observed to be asymmetric, with an abbreviated high power tail.In this case, any channel having power higher than the high power tailof the PDF 212 may be considered to have an interference signal.

The CCDFs 206 denote the likelihood that a power measurement in achannel will exceed a given mean power α, by some value α/σ, wherein σis standard deviation of the power distribution. Specifically, the CCDFs206 include an instance of CCDF for a DSSS signal carrying one mobileunit (220), an instance of CCDF for a DSSS signal carrying ten mobileunits (222), and an instance of CCDF for a DSSS signal carrying twentymobile units (224). Thus, for example, for a DSSS signal carrying onemobile unit, the likelihood of any narrowband channel having the ratioα/σ of 10 dB or more is 0.01%. Therefore, an optimal filter can be tunedto such a narrowband channel having excess power.

One method of detecting such a narrowband channel having interference isby exploiting the noise like characteristic of a DSSS signal. Due tosuch noise like characteristic of DSSS signal, a particular measurementof a narrowband channel power gives no predictive power as to what thenext measurement of the same measurement channel may be. In other words,consecutive observations of power in a given narrowband channels areun-correlated. As a result, if a given measurement of power in anarrowband channel provides predictive power over subsequentmeasurements of power in that particular channel, thus indicating adeparture from statistics expected of a narrowband channel withoutinterference, such a narrowband channel may be determined to containinterference.

FIG. 10 illustrates a flowchart of an interference detection program 300that may be used to determine location of interference in a DSSS signal.At block 302 a series of DSSS signals can be scanned by the adaptivefront end controller described above and the observed values of thesignal strengths can be stored for each of various narrowband channelslocated in the DSSS signal. For example, at block 302 the adaptive frontend controller may continuously scan the 1.2288 MHz DSSS signal 60 foreach of the 41 narrowband channels dispersed within it. The adaptivefront end controller may be implemented by any well known analog scanneror digital signal processor (DSP) used to scan and store signalstrengths in a DSSS signal. The scanned values of narrowband signalstrengths may be stored in a memory of such DSP or in any other computerreadable memory. The adaptive front end controller may store the signalstrength of a particular narrowband channel along with any information,such as a numeric identifier, identifying the location of thatparticular narrowband channel within the DSSS signal.

At block 304 the adaptive front end controller can determine the numberof sequences m of a DSSS signal that may be required to be analyzed todetermine narrowband channels having interference. A user may providesuch a number m based on any pre-determined criteria. For example, auser may provide m to be equal to four, meaning that four consecutiveDSSS signals need to be analyzed to determine if any of the narrowbandchannels within that DSSS signal spectrum includes an interferencesignal. As one of ordinary skill in the art would appreciate, the higheris the selected value of m, the more accurate will be the interferencedetection. However, the higher the number m is, the higher is the delayin determining whether a particular DSSS signal had an interferencepresent in it, subsequently, resulting in a longer delay before a filteris applied to the DSSS signal to remove the interference signal.

Generally, detection of an interference signal may be performed on arolling basis. That is, at any point in time, m previous DSSS signalsmay be used to analyze presence of an interference signal. The earliestof such m interference signals may be removed from the set of DSSSsignals used to determine the presence of an interference signal on afirst-in-first-out basis. However, in an alternate embodiment, analternate sampling method for the set of DSSS signals may also be used.

At block 306 the adaptive front end controller can select x narrowbandchannels having the highest signal strength from each of the m mostrecent DSSS signals scanned at the block 302. The number x may bedetermined by a user. For example, if x is selected to be equal tothree, the block 306 may select three highest channels from each of them most recent DSSS signals. The methodology for selecting x narrowbandchannels having highest signal strength from a DSSS signal is describedin further detail in FIG. 11 below. For example, the adaptive front endcontroller at block 306 may determine that the first of the m DSSSsignals has narrowband channels 10, 15 and 27 having the highest signalstrengths, the second of the m DSSS channels has narrowband channels 15and 27 and 35 having the highest signal strengths, and the third of them DSSS channels has the narrowband channels 15, 27 and 35 having thehighest narrowband signal strength.

After having determined the x narrowband channels having the highestsignal strengths in each of the m DSSS signals, at block 308 theadaptive front end controller can compare these x narrowband channels todetermine if any of these highest strength narrowband channels appearmore than once in the m DSSS signals. In case of the example above, theadaptive front end controller at block 308 may determine that thenarrowband channels 15 and 27 are present among the highest strengthnarrowband channels for each of the last three DSSS signals, whilechannel 35 is present among the highest strength narrowband channels forat least two of the last three DSSS signals.

Such consistent appearance of narrowband channels having highest signalstrength over subsequent DSSS signals indicate that narrowband channels15 and 27, and probably the narrowband channel 35, may have aninterference signal super-imposed on them. At block 310 the adaptivefront end controller may use such information to determine whichnarrowband channels may have interference. For example, based on thenumber of times a given narrowband channel appears in the selectedhighest signal strength channels, the adaptive front end controller atblock 310 may determine the confidence level that may be assigned to aconclusion that a given narrowband channel contains an interferencesignal.

Alternatively, at block 310 the adaptive front end controller maydetermine a correlation factor for each of the various narrowbandchannels appearing in the x selected highest signal strength channelsand compare the calculated correlation factors with a thresholdcorrelation factor to determine whether any of the x selected channelshas correlated signal strengths. Calculating a correlation factor basedon a series of observations is well known to those of ordinary skill inthe art and therefore is not illustrated in further detail herein. Thethreshold correlation factor may be given by the user of theinterference detection program 300.

Note that while in the above illustrated embodiment, the correlationfactors of only the selected highest signal strength channels arecalculated, in an alternate embodiment, correlation factors of all thenarrowband channels within the DSSS signals may be calculated andcompared to the threshold correlation factor.

Empirically, it may be shown that when m is selected to be equal tothree, for a clean DSSS signal, the likelihood of having at least onematch among the higher signal strength narrowband channels is 0.198, thelikelihood of having at least two matches among the higher signalstrength narrowband channels is 0.0106, and the likelihood of having atleast three matches among the higher signal strength narrowband channelsis 9.38×10⁻⁵. Thus, the higher the number of matches, the lesser is thelikelihood of having a determination that one of the x channels containsan interference signal (i.e., a false positive interference detection).It may be shown that if the number of scans m is increased to, say fourDSSS scans, the likelihood of having such matches in m consecutive scansis even smaller, thus providing higher confidence that if such matchesare found to be present, they indicate presence of interference signalin those narrowband channels.

To identify the presence of interference signals with even higher levelof confidence, at block 312 the adaptive front end controller may decidewhether to compare the signal strengths of the narrowband channelsdetermined to have an interference signal with a threshold. If at block312 the adaptive front end controller decides to perform such acomparison, at block 314 the adaptive front end controller may comparethe signal strength of each of the narrowband channels determined tohave an interference with a threshold level. Such comparing of thenarrowband channel signal strengths with a threshold may provide addedconfidence regarding the narrowband channel having an interferencesignal so that when a filter is configured according to the narrowbandchannel, the probability of removing a non-interfering signal isreduced. However, a user may determine that such added confidence levelis not necessary and thus no such comparison to a threshold needs to beperformed. In which case, at block 316 the adaptive front end controllerstores the interference signals in a memory.

After storing the information about the narrowband channels havinginterference signals, at block 318 the adaptive front end controllerselects the next DSSS signal from the signals scanned and stored atblock 302. At block 318 the adaptive front end controller may cause thefirst of the m DSSS signals to be dropped and the newly added DSSSsignal is added to the set of m DSSS signals that will be used todetermine presence of an interference signal (first-in-first-out).Subsequently, at block 306 the process of determining narrowbandchannels having interference signals is repeated by the adaptive frontend controller. Finally, at block 320 the adaptive front end controllermay select and activate one or more filters that are located in the pathof the DSSS signal to filter out any narrowband channel identified ashaving narrowband interference in it.

Now referring to FIG. 11, a flowchart illustrates a high strengthchannels detection program 350 that may be used to identify variouschannels within a given scan of the DSSS signal that may contain aninterference signal. The high strength channels detection program 350may be used to implement the functions performed at block 306 of theinterference detection program 300. In a manner similar to theinterference detection program 300, the high strength channels detectionprogram 350 may also be implemented using software, hardware, firmwareor any combination thereof.

At block 352 the adaptive front end controller may sort signal strengthsof each of the n channels within a given DSSS signal. For example, if aDSSS signal has 41 narrowband channels, at block 352 the adaptive frontend controller may sort each of the 41 narrowband channels according toits signal strengths. Subsequently, at block 354 the adaptive front endcontroller may select the x highest strength channels from the sortednarrowband channels and store information identifying the selected xhighest strength channels for further processing. An embodiment of thehigh strength channels detection program 350 may simply use the selectedx highest strength channels from each scan of the DSSS signals todetermine any presence of interference in the DSSS signals. However, inan alternate embodiment, additional selected criteria may be used.

Subsequently, at block 356 the adaptive front end controller candetermine if it is necessary to compare the signal strengths of the xhighest strength narrowband channels to any other signal strength value,such as a threshold signal strength, etc., where such a threshold may bedetermined using the average signal strength across the DSSS signal. Forexample, at block 356 the adaptive front end controller may use acriterion such as, for example: “when x is selected to be four, if atleast three out of four of the selected narrowband channels have alsoappeared in previous DSSS signals, no further comparison in necessary.”Another criterion may be, for example: “if any of the selectednarrowband channels is located at the fringe of the DSSS signal, thesignal strengths of such narrowband channels should be compared to athreshold signal strength.” Other alternate criteria may also beprovided.

If at block 356 the adaptive front end controller determines that nofurther comparison of the signal strengths of the selected x narrowbandchannels is necessary, at block 358 the adaptive front end controllerstores information about the selected x narrowband channels in a memoryfor further processing. If at block 356 the adaptive front endcontroller determines that it is necessary to apply further selectioncriteria to the selected x narrowband channels, the adaptive front endcontroller returns to block 360. At block 360 the adaptive front endcontroller may determine a threshold value against which the signalstrengths of each of the x narrowband channels are compared based on apredetermined methodology.

For example, in an embodiment, at block 360 the adaptive front endcontroller may determine the threshold based on the average signalstrength of the DSSS signal. The threshold signal strength may be theaverage signal strength of the DSSS signal or a predetermined value maybe added to such average DSSS signal to derive the threshold signalstrength.

Subsequently, at block 362 the adaptive front end controller may comparethe signal strengths of the selected x narrowband channels to thethreshold value determined at block 360. Only the narrowband channelshaving signal strengths higher than the selected threshold are used indetermining presence of interference in the DSSS signal. Finally, atblock 364 the adaptive front end controller may store information aboutthe selected x narrowband channels having signal strengths higher thanthe selected threshold in a memory. As discussed above, the interferencedetection program 300 may use such information about the selectednarrowband channels to determine the presence of interference signal inthe DSSS signal.

The interference detection program 300 and the high strength channeldetection program 350 may be implemented by using software, hardware,firmware or any combination thereof. For example, such programs may bestored on a memory of a computer that is used to control activation anddeactivation of one or more notch filters. Alternatively, such programsmay be implemented using a digital signal processor (DSP) whichdetermines the presence and location of interference channels in adynamic fashion and activates/de-activates one or more filters.

FIG. 12 illustrates a three dimensional graph 370 depicting several DSSSsignals 372-374 over a time period. A first axis of the graph 370illustrates the number of narrowband channels of the DSSS signals372-374, a second axis illustrates time over which a number of DSSSsignals 372-374 are scanned, and a third axis illustrates the power ofeach of the narrowband channels. The DSSS signals 372-374 are shown tobe affected by an interference signal 378.

The interference detection program 370 may start scanning various DSSSsignals 372-374 starting from the first DSSS signal 372. As discussedabove at block 304 the adaptive front end controller determines thenumber m of the DSSS signals 372-374 that are to be scanned. Because theinterference signal 378 causes the signal strength of a particularnarrowband channel to be consistently higher than the other channels fora number of consecutive scans of the DSSS signals 372-374 at block 210the adaptive front end controller identifies a particular channel havingan interference signal present. Subsequently, at block 320 the adaptivefront end controller will select and activate a filter that applies thefilter function as described above, to the narrowband channel havinginterference.

The graph 370 also illustrates the average signal strengths of each ofthe DSSS signals 372-374 by a line 376. As discussed above, at block 362the adaptive front end controller may compare the signal strengths ofeach of the x selected narrowband channels from the DSSS signals 372-374with the average signal strength, as denoted by line 376, in thatparticular DSSS signal.

Now referring to FIG. 13, a graph 380 illustrates interference detectionsuccess rate of using the interference detection program 370, as afunction of strength of an interference signal affecting a DSSS signal.The x-axis of the graph 380 depicts the strength of interference signalrelative to the strength of the DSSS signal, while the y-axis depictsthe detection success rate in percentages. As illustrated, when aninterference signal has a strength of at least 2 dB higher than thestrength of the DSSS signal, such an interference signal is detectedwith at least ninety five percent success rate.

The foregoing interference detection and mitigation embodiments canfurther be adapted for detecting and mitigating interference inlong-term evolution (LTE) communication systems.

LTE transmission consists of a combination of Resource Blocks (RB's)which have variable characteristics in frequency and time. A single RBcan be assigned to a user equipment, specifically, a 180 kHz continuousspectrum utilized for 0.5-1 msec. An LTE band can be partitioned into anumber of RBs which could be allocated to individual communicationdevices for specified periods of time for LTE transmission.Consequently, an LTE spectrum has an RF environment dynamically variablein frequency utilization over time. FIG. 14 depicts an illustrative LTEtransmission.

LTE utilizes different media access methods for downlink (orthogonalfrequency-division multiple access; generally, referred to as OFDMA) anduplink (single carrier frequency-division multiple access; generally,referred to as SC-FDMA). For downlink communications, each RB contains12 sub-carriers with 15 kHz spacing. Each sub-carrier can be used totransmit individual bit information according to the OFDMA protocol. Foruplink communications, LTE utilizes a similar RB structure with 12sub-carriers, but in contrast to downlink, uplink data is pre-coded forspreading across 12 sub-carriers and is transmitted concurrently on all12 sub-carriers.

The effect of data spreading across multiple sub-carriers yields atransmission with spectral characteristics similar to a CDMA/UMTSsignal. Hence, similar principles of narrow band interference detectioncan be applied within an instance of SC-FDMA transmission from anindividual communication device—described herein as user equipment (UE).However, since each transmission consists of unknown RB allocations withunknown durations, such a detection principle can only be appliedseparately for each individual RB within a frequency and specific timedomain. If a particular RB is not used for LTE transmission at the timeof detection, the RF spectrum will present a thermal noise which adheresto the characteristics of a spread spectrum signal, similar to aCDMA/UMTS signal.

Co-channel, as well as other forms of interference, can causeperformance degradation to SC-FDMA and OFDMA signals when present. FIG.15 depicts an illustration of an LTE transmission affected byinterferers 402, 404, 406 and 408 occurring at different points in time.Since such LTE transmissions do not typically have flat power spectraldensities (see FIG. 14), identification of interference as shown in FIG.15 can be a difficult technical problem. The subject disclosure,presents a method to improve the detection of narrowband interference inSC-FDMA/OFDM channels through a time-averaging algorithm that isolatesinterference components in the channel and ignores the underlyingsignal.

Time averaging system (TAS) can be achieved with a boxcar (rolling)average, in which the TAS is obtained as a linear average of a Q ofprevious spectrum samples, with Q being a user-settable parameter. The Qvalue determines the “strength” of the averaging, with higher Q valueresulting in a TAS that is more strongly smoothed in time and lessdependent on short duration transient signals. Due to thefrequency-hopped characteristic of SC-FDMA/OFDMA signals, which arecomposed of short duration transients, the TAS of such signals isapproximately flat. It will be appreciated that TAS can also beaccomplished by other methods such as a forgetting factor filter.

In one embodiment, an adaptive threshold can be determined by a method500 as depicted in FIG. 16. Q defines how many cycles of t_(i) to use(e.g., 100 cycles can be represented by t₁ through t₁₀₀). The adaptivefront end module 56 of FIG. 6 can be configured to measure power in 30kHz increments starting from a particular RB and over multiple timecycles. For illustration purposes, the adaptive front end module 56 isassumed to measure power across a 5 MHz spectrum. It will be appreciatedthat the adaptive front end module 56 can be configured for otherincrements (e.g., 15 kHz or 60 kHz), and a different RF spectrumbandwidth. With this in mind, the adaptive front end module 56 can beconfigured at frequency increment f1 to measure power at t1, t2, throughtq (q representing the number of time cycles, i.e., Q). At f1+30 kHz,the adaptive front end module 56 measures power at t1, t2, . . . tn. Thefrequency increment can be defined by f0+(z−1)*30 kHz=fz, where f0 is astarting frequency, where z=1 . . . x, and z defines increments of 30kHz increment, e.g., f1=f(z=1) first 30 kHz increment, f2=f(z=2) second30 kHz increment, etc.

The adaptive front end module 56 repeats these steps until the spectrumof interest has been fully scanned for Q cycles, thereby producing thefollowing power level sample sets:S _(f1(t1 . . . tq)) :s _(1,t1,f1) ,s _(2,t2,f1) , . . . ,s _(q,tq,f1)S _(f2(t1 . . . tq)) :s _(1,t1,f2) ,s _(2,t2,f2) , . . . ,s _(q,tq,f2)S _(fx(t1 . . . tq)) :s _(1,t1,fz) ,s _(2,t2,fx) , . . . ,s _(q,tq,fx)

The adaptive front end module 56 in step 504, calculates averages foreach of the power level sample sets as provided below:a1(f1)=(s _(1,t1,f1) +s _(2,t2,f1) , . . . ,+s _(q,tq,f1))/qa2(f2)=(s _(1,t1,f2) +s _(2,t2,f2) , . . . ,s _(q,tq,f2))/qax(fx)=(s _(1,t1,fx) +s _(2,t2,fx) , . . . ,s _(2,tq,fx))/q

In one embodiment, the adaptive front end module 56 can be configured todetermine at step 506 the top “m” averages (e.g., the top 3 averages)and dismiss these averages from the calculations. The variable “m” canbe user-supplied or can be empirically determined from fieldmeasurements collected by one or more base stations utilizing anadaptive front end module 56. This step can be used to avoid skewing abaseline average across all frequency increments from being too high,resulting in a threshold calculation that may be too conservative. Ifstep 506 is invoked, a baseline average can be determined in step 508according to the equation: Baseline Avg=(a1+a2+ . . . +az−averages thathave been dismissed)/(x−m). If step 506 is skipped, the baseline averagecan be determined from the equation: Baseline Avg=(a1+a2+ . . . +az)/x.Once the baseline average is determined in step 508, the adaptive frontend module 56 can proceed to step 510 where it calculates a thresholdaccording to the equation: Threshold=ydB offset+Baseline Avg. The ydBoffset can be user defined or empirically determined from fieldmeasurements collected by one or more base stations utilizing anadaptive front end module 56.

Once a cycle of steps 502 through 510 have been completed, the adaptivefront end module 56 can monitor at step 512 interference per frequencyincrement of the spectrum being scanned based on any power levelsmeasured above the threshold 602 calculated in step 510 as shown in FIG.17. Not all interferers illustrated in FIG. 17 exceed the threshold,such as the interferer with reference 610. Although this interferer hasa high power signature, it was not detected because it occurred during aresource block (R4) that was not in use. As such, the interferer 510fell below the threshold 602. In another illustration, interferer s 612also fell below the threshold 602. This interferer was missed because ofits low power signature even though the RB from which it occurred (R3)was active.

Method 500 can utilize any of the embodiments in the illustratedflowcharts described above to further enhance the interferencedetermination process. For example, method 500 of FIG. 16 can be adaptedto apply weights to the power levels, and/or perform correlationanalysis to achieve a desired confidence level that the properinterferers are addressed. For example, with correlation analysis, theadaptive front end module 56 can be configured to ignore interferers 614and 616 of FIG. 17 because their frequency of occurrence is low. Method500 can also be adapted to prioritize interference mitigation.Prioritization can be based on frequency of occurrence of theinterferers, time of day of the interference, the affect theinterference has on network traffic, and/or other suitable factors forprioritizing interference to reduce its impact on the network.Prioritization schemes can be especially useful when the filteringresources of the adaptive front end module 56 can only support a limitednumber of filtering events.

When one or more interferers are detected in step 512, the adaptivefront end module 56 can mitigate the interference at step 514 byconfiguring one or more filters to suppress the one or more interferersas described above. When there are limited resources to suppress allinterferers, the adaptive front end module 56 can use a prioritizationscheme to address the most harmful interference as discussed above. FIG.18 provides an illustration of how the adaptive front end module 56 canbe suppress interferers based on the aforementioned algorithms of thesubject disclosure. For example, interferers 612, 614 and 616 can beignored by the adaptive front end module 56 because their correlationmay be low, while interference suppression is applied for all otherinterferers as shown at 650, 660 in FIG. 18.

In one embodiment, the adaptive front end module 56 can submit a reportto a diagnostic system that includes information relating to theinterferers detected. The report can include, among other things, afrequency of occurrence of the interferer, spectral data relating to theinterferer, an identification of the base station from which theinterferer was detected, a severity analysis of the interferer (e.g.,bit error rate, packet loss rate, or other traffic information detectedduring the interferer), and so on. The diagnostic system can communicatewith other base stations with other operable adaptive front end module56 to perform macro analysis of interferers such as triangulation tolocate interferers, identity analysis of interferers based on acomparison of spectral data and spectral profiles of known interferers,and so on.

In one embodiment, the reports provided by the adaptive front end module56 can be used by the diagnostic system to in some instance performavoidance mitigation. For example, if the interferer is known to be acommunication device in the network, the diagnostic system can direct abase station in communication with the communication device to directthe communication device to another channel so as to remove theinterference experienced by a neighboring base station. Alternatively,the diagnostic system can direct an affected base station to utilizebeam steering and or mechanical steering of antennas to avoid aninterferer. When avoidance is performed, the mitigation step 514 can beskipped or may be invoked less as a result of the avoidance steps takenby the diagnostic system.

Once mitigation and/or an interference report has been processed insteps 514 and 516, respectively, the adaptive front end module 56 canproceed to step 518. In this step, the adaptive front end module 56 canrepeat steps 502 through 510 to calculate a new baseline average andcorresponding threshold based on Q cycles of the resource blocks. Eachcycle creates a new adaptive threshold that is used for interferencedetection. It should be noted that when Q is high, changes to thebaseline average are smaller, and consequently the adaptive thresholdvaries less over Q cycles. In contrast, when Q is low, changes to thebaseline average are higher, which results in a more rapidly changingadaptive threshold.

Generally speaking, one can expect that there will be more noise-freeresource blocks than resource blocks with substantive noise.Accordingly, if an interferer is present (constant or ad hoc), one canexpect the aforementioned algorithm described by method 500 will producean adaptive threshold (i.e., baseline average+offset) that will be lowerthan interferer's power level due to mostly noise-free resource blocksdriving down baseline average. Although certain communication deviceswill have a high initial power level when initiating communications witha base station, it can be further assumed that over time the powerlevels will be lowered to a nominal operating condition. A reasonablyhigh Q would likely also dampen disparities between RB's based on theabove described embodiments.

It is further noted that the aforementioned algorithms can be modifiedwhile maintaining an objective of mitigating detected interference. Forinstance, instead of calculating a baseline average from a combinationof averages a1(f1) through ax(fx) or subsets thereof, the adaptive frontend controller 56 can be configured to calculate a base line average foreach resource block according to a known average of adjacent resourceblocks, an average calculated for the resource block itself, or otherinformation that may be provided by, for example, a resource blockscheduler that may be helpful in calculating a desired baseline averagefor each resource block or groups of resource blocks. For instance, theresource block schedule can inform the adaptive front end module 56 asto which resource blocks are active and at what time periods. Thisinformation can be used by the adaptive front end module 56 determineindividualized baseline averages for each of the resource blocks orgroups thereof. Since baseline averages can be individualized, eachresource block can also have its own threshold applied to the baselineaverage of the resource block. Accordingly, thresholds can vary betweenresource blocks for detecting interferers.

It is further noted that the aforementioned mitigation and detectionalgorithms can be implemented by any communication device includingcellular phones, smartphones, tablets, small base stations, macro basestations, femto cells, WiFi access points, and so on. Small basestations (commonly referred to as small cells) can represent low-poweredradio access nodes that can operate in licensed and/or unlicensedspectrum that have a range of 10 meters to 1 or 2 kilometers, comparedto a macrocell (or macro base station) which might have a range of a fewtens of kilometers. Small base stations can be used for mobile dataoffloading as a more efficient use of radio spectrum.

FIG. 19A depicts an illustrative embodiment of a system for detectingand reporting interference. In this embodiment, a wideband base station16 is in communication with wideband mobile units 13A-13D. Narrowbandmobile unit 12 represents a source of interference (for example,interference 46 in CDMA channel 42C, as shown in FIG. 3). In accordancewith an interference detection procedure (discussed in detail below),base station 16 can monitor the location of mobile units 13A-13D, andrequest that a selected number of those units perform spectral analysisto identify the interferer. In situations where a plurality of mobileunits are located close to each other (for example, mobile units 13B,13D both located in a physical space 17), only one of those units may beselected. In accordance with a request from the base station, selectedmobile units 13A, 13B, 13C can detect interference source 12 and providedata for identifying and locating the interference source to the basestation.

FIG. 19B depicts an illustrative embodiment of a method 700 fordetecting and reporting interference such as shown in FIG. 15, using asystem including mobile devices 13A-13D and wideband base station 16 asshown in FIG. 19A. Method 700 can be performed by mobile communicationdevices, stationary communication devices or a combination thereof, bybase stations, and/or by a system or systems in communication with thebase stations and/or mobile communication devices. Method 700 can beginwith monitoring the locations of mobile devices (step 702), where themobile devices are used to detect interference in one or more segmentsof a communication system. A communication system in the present contextcan represent a base station, such as a cellular base station, a smallcell (which can represent a femto cell, or a smaller more portableversion of a cellular base station), a WiFi router, a cordless phonebase station, or any other form of a communication system that canprovide communication services (voice, data or both) to fixed or mobilecommunication devices. The terms communication system and base stationmay be used interchangeably below. In either instance, such terms are tobe given a broad interpretation such as described above. A segment canrepresent a resource block or other subsets of communication spectrum ofany suitable bandwidth. For illustration purposes only, segments will bereferred to henceforth as resource blocks. In addition, reference willbe made by a mobile communication device affected by the interference.It is to be understood that method 700 can also be applied to stationarycommunication devices.

A set of mobile devices is selected (step 704) to perform spectralanalysis to identify interferers. The selection is made from the devicescurrently in communication with the base station (for example, devices13A-13D communicating with base station 16). If a plurality of devicesis located at substantially the same location (that is, the distancebetween devices is less than a predetermined threshold), then a randomselection can be made from those devices (steps 706, 708). For example,as shown in FIG. 19A, device 13B is selected at random from deviceslocated close to each other at 17. The base station can then requestdata from the selected devices (step 710). This request can be madeperiodically, at regular or varying intervals, or when some othercriterion is met. A different set of mobile devices may be selected eachtime the request is made.

The base station then collects data from the selected mobile devices(step 712) regarding interference. In this embodiment, each of themobile devices responds to the base station request by performingspectral analysis on detected interference signals to determine thestrength and relative direction of each signal and its spectral profile.Each reporting mobile device also provides a time stamp indicating thetime that the interference signal was detected. The base station canthen use the spectral profile to identify the source of the interference(step 714). For example, the base station can compare the reportedspectral profile with entries in a database of previously identifiedinterferers; the database can be maintained by a server supporting thecontrol facility for the communication network (see FIG. 4).

If a plurality of mobile devices reports the presence of the sameinterference source (based on similar spectral profiles detected by thedevices), the base station can then use the locations of those devicesto locate the interferer by a triangulation procedure (step 716). Sincethe interference can be measured from different vantage points ofseveral mobile devices (and remeasured if desired using other devicesoffering another set of vantage points), the interference source can belocated more accurately than with fixed stations. The base station thuscan determine the frequency of the interference signal and its spectralprofile, the location of the interference source, the time that theinterference occurred, and the frequency of occurrence of theinterference. The base station then can generate a report on theinterference (step 718) and send the report to the reporting and controlfacility. Data regarding the interference can be stored and organized(step 720) in a system-wide database (along with the individualdatabases of each base station) according to time stamps when theinterference occurred, resource blocks affected by the interference, anidentity of the base station collecting the interference information, anidentity of the mobile communication device affected by theinterference, frequency of occurrence of the interference, spectralinformation descriptive of the interference, an identity of theinterferer if it can be synthesized from the spectral information, andso on.

An illustrative embodiment of a communication device 800 is shown inFIG. 20. Communication device 800 can serve in whole or in part as anillustrative embodiment of the devices depicted in FIGS. 1, 4, and 6-8.In one embodiment, the communication device 800 can be configured, forexample, to perform operations such as measuring a power level in atleast a portion of a plurality of resource blocks occurring in a radiofrequency spectrum, where the measuring occurs for a plurality of timecycles to generate a plurality of power level measurements, calculatinga baseline power level according to at least a portion of the pluralityof power levels, determining a threshold from the baseline power level,and monitoring at least a portion of the plurality of resource blocksfor signal interference according to the threshold. Other embodimentsdescribed in the subject disclosure can be used by the communicationdevice 800.

To enable these features, communication device 800 can comprise awireline and/or wireless transceiver 802 (herein transceiver 802), auser interface (UI) 804, a power supply 814, a location receiver 816, amotion sensor 818, an orientation sensor 820, and a controller 806 formanaging operations thereof. The transceiver 802 can support short-rangeor long-range wireless access technologies such as Bluetooth, ZigBee,WiFi, DECT, or cellular communication technologies, just to mention afew. Cellular technologies can include, for example, CDMA-1×,UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well asother next generation wireless communication technologies as they arise.The transceiver 802 can also be adapted to support circuit-switchedwireline access technologies (such as PSTN), packet-switched wirelineaccess technologies (such as TCP/IP, VoIP, etc.), and combinationsthereof.

The UI 804 can include a depressible or touch-sensitive keypad 808 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device800. The keypad 808 can be an integral part of a housing assembly of thecommunication device 800 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth. The keypad 808 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 804 can further include a display810 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 800. In anembodiment where the display 810 is touch-sensitive, a portion or all ofthe keypad 808 can be presented by way of the display 810 withnavigation features.

The display 810 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 800 can be adapted to present a user interface withgraphical user interface (GUI) elements that can be selected by a userwith a touch of a finger. The touch screen display 810 can be equippedwith capacitive, resistive or other forms of sensing technology todetect how much surface area of a user's finger has been placed on aportion of the touch screen display. This sensing information can beused to control the manipulation of the GUI elements or other functionsof the user interface. The display 810 can be an integral part of thehousing assembly of the communication device 800 or an independentdevice communicatively coupled thereto by a tethered wireline interface(such as a cable) or a wireless interface.

The UI 804 can also include an audio system 812 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 812 can further include amicrophone for receiving audible signals of an end user. The audiosystem 812 can also be used for voice recognition applications. The UI804 can further include an image sensor 813 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 814 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 800 to facilitatelong-range or short-range portable applications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 816 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 800 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 818can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 800 in three-dimensional space. Theorientation sensor 820 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device800 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 800 can use the transceiver 802 to alsodetermine a proximity to a cellular, WiFi, Bluetooth, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 806 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 400.

Other components not shown in FIG. 20 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 800 can include a reset button (not shown). The reset button canbe used to reset the controller 806 of the communication device 800. Inyet another embodiment, the communication device 800 can also include afactory default setting button positioned, for example, below a smallhole in a housing assembly of the communication device 800 to force thecommunication device 800 to re-establish factory settings. In thisembodiment, a user can use a protruding object such as a pen or paperclip tip to reach into the hole and depress the default setting button.The communication device 400 can also include a slot for adding orremoving an identity module such as a Subscriber Identity Module (SIM)card. SIM cards can be used for identifying subscriber services,executing programs, storing subscriber data, and so forth.

The communication device 800 as described herein can operate with moreor less of the circuit components shown in FIG. 20. These variantembodiments can be used in one or more embodiments of the subjectdisclosure.

It should be understood that devices described in the exemplaryembodiments can be in communication with each other via various wirelessand/or wired methodologies. The methodologies can be links that aredescribed as coupled, connected and so forth, which can includeunidirectional and/or bidirectional communication over wireless pathsand/or wired paths that utilize one or more of various protocols ormethodologies, where the coupling and/or connection can be direct (e.g.,no intervening processing device) and/or indirect (e.g., an intermediaryprocessing device such as a router).

FIG. 21 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 900 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as the devices of FIGS. 1, 4, and 6-8. In someembodiments, the machine may be connected (e.g., using a network 926) toother machines. In a networked deployment, the machine may operate inthe capacity of a server or a client user machine in server-client usernetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet PC, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

The computer system 900 may include a processor (or controller) 902(e.g., a central processing unit (CPU), a graphics processing unit (GPU,or both), a main memory 904 and a static memory 906, which communicatewith each other via a bus 908. The computer system 900 may furtherinclude a display unit 910 (e.g., a liquid crystal display (LCD), a flatpanel, or a solid state display. The computer system 900 may include aninput device 912 (e.g., a keyboard), a cursor control device 914 (e.g.,a mouse), a disk drive unit 916, a signal generation device 918 (e.g., aspeaker or remote control) and a network interface device 920. Indistributed environments, the embodiments described in the subjectdisclosure can be adapted to utilize multiple display units 910controlled by two or more computer systems 900. In this configuration,presentations described by the subject disclosure may in part be shownin a first of the display units 910, while the remaining portion ispresented in a second of the display units 910.

The disk drive unit 916 may include a tangible computer-readable storagemedium 922 on which is stored one or more sets of instructions (e.g.,software 924) embodying any one or more of the methods or functionsdescribed herein, including those methods illustrated above. Theinstructions 924 may also reside, completely or at least partially,within the main memory 904, the static memory 906, and/or within theprocessor 902 during execution thereof by the computer system 900. Themain memory 904 and the processor 902 also may constitute tangiblecomputer-readable storage media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices that can likewise be constructed to implement themethods described herein. Application specific integrated circuits andprogrammable logic array can use downloadable instructions for executingstate machines and/or circuit configurations to implement embodiments ofthe subject disclosure. Applications that may include the apparatus andsystems of various embodiments broadly include a variety of electronicand computer systems. Some embodiments implement functions in two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals communicated between and through the modules,or as portions of an application-specific integrated circuit. Thus, theexample system is applicable to software, firmware, and hardwareimplementations.

In accordance with various embodiments of the subject disclosure, theoperations or methods described herein are intended for operation assoftware programs or instructions running on or executed by a computerprocessor or other computing device, and which may include other formsof instructions manifested as a state machine implemented with logiccomponents in an application specific integrated circuit or fieldprogrammable gate array. Furthermore, software implementations (e.g.,software programs, instructions, etc.) including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein. It is furthernoted that a computing device such as a processor, a controller, a statemachine or other suitable device for executing instructions to performoperations or methods may perform such operations directly or indirectlyby way of one or more intermediate devices directed by the computingdevice.

While the tangible computer-readable storage medium 622 is shown in anexample embodiment to be a single medium, the term “tangiblecomputer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “tangible computer-readable storage medium” shallalso be taken to include any non-transitory medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of the methods ofthe subject disclosure.

The term “tangible computer-readable storage medium” shall accordinglybe taken to include, but not be limited to: solid-state memories such asa memory card or other package that houses one or more read-only(non-volatile) memories, random access memories, or other re-writable(volatile) memories, a magneto-optical or optical medium such as a diskor tape, or other tangible media which can be used to store information.Accordingly, the disclosure is considered to include any one or more ofa tangible computer-readable storage medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are from time-to-timesuperseded by faster or more efficient equivalents having essentiallythe same functions. Wireless standards for device detection (e.g.,RFID), short-range communications (e.g., Bluetooth, WiFi, Zigbee), andlong-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used bycomputer system 900.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Theexemplary embodiments can include combinations of features and/or stepsfrom multiple embodiments. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. Figuresare also merely representational and may not be drawn to scale. Certainproportions thereof may be exaggerated, while others may be minimized.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,can be used in the subject disclosure.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, it can beseen that various features are grouped together in a single embodimentfor the purpose of streamlining the disclosure. This method ofdisclosure is not to be interpreted as reflecting an intention that theclaimed embodiments require more features than are expressly recited ineach claim. Rather, as the following claims reflect, inventive subjectmatter lies in less than all features of a single disclosed embodiment.Thus the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separately claimedsubject matter.

What is claimed is:
 1. A mobile device comprising: a memory to storeinstructions; and a processor coupled to the memory, wherein responsiveto executing the instructions, the processor performs operationscomprising: receiving a request from a base station to perform aspectral analysis of signals over a spectrum of frequencies, wherein themobile device is selected by the base station from among a plurality ofmobile devices for performing the spectral analysis based on a locationof the mobile device; detecting an interference among the signals basedon a threshold relative to a baseline power level; and providing, inresponse to the request, data to the base station regarding a source ofthe interference, wherein the data comprises spectral data foridentifying the source of the interference, a time of occurrence of theinterference, a frequency of occurrence of the interference or anycombination thereof.
 2. The mobile device of claim 1, wherein theoperations further comprise providing location data to the base station,and wherein the location of the mobile device is determined by the basestation according to the location data.
 3. The mobile device of claim 1,wherein the plurality of mobile devices provides location information tothe base station, and wherein the base station determines the locationof the source of the interference according to a triangulation procedureutilizing at least some of the location information.
 4. The mobiledevice of claim 1, wherein the plurality of mobile devices and the basestation comprise a diagnostic system having access to a database ofinterference sources including spectral profiles.
 5. The mobile deviceof claim 4, wherein the diagnostic system identifies the source of theinterference based on a comparison of the spectral data and the spectralprofiles.
 6. The mobile device of claim 1, wherein the signals arereceived over a communication channel, and wherein the operationsfurther comprise performing a severity analysis of the interference withrespect to the communication channel.
 7. The mobile device of claim 6,wherein the severity analysis comprises determining a bit error rate ofthe communication channel, a packet loss rate of the communicationchannel, traffic analysis for the communication channel, or acombination thereof.
 8. The mobile device of claim 1, wherein the mobiledevice and a second mobile device of the plurality of mobile devices arelocated within a predetermined distance from each other, and the mobiledevice is selected according to a random selection between the mobiledevice and the second mobile device.
 9. The mobile device of claim 1,wherein the data regarding the source of the interference furthercomprises a strength of the interference, and the time of occurrence andthe frequency of occurrence of the interference are based on a timestamp provided by the mobile device.
 10. A base station comprising: amemory to store instructions; and a processor coupled to the memory,wherein responsive to executing the instructions, the processor performsoperations comprising: selecting a mobile device for performing aspectral analysis according to a location of the mobile device; sendinga request to the mobile device to perform the spectral analysis ofsignals; receiving data, provided by the mobile device in response tothe request, wherein the data comprises spectral data for identifying asource of interference detected among the signals; accessing a databaseof interference source information; identifying the source of theinterference based on a comparison of the spectral data and theinterference source information; and generating a report includinginformation regarding the interference, wherein the report is providedto a diagnostic system to mitigate effects of the interference.
 11. Thebase station of claim 10, wherein the interference source informationincludes spectral profiles, wherein the mobile device is one of aplurality of mobile devices in communication with the base station, andwherein the operations further comprise: receiving, from the pluralityof mobile devices, location information; and determining the location ofthe interference source utilizing a triangulation procedure according toat least some of the location information.
 12. The base station of claim11, wherein the operations further comprise selecting the mobile devicefrom the plurality of devices, in accordance with the locationinformation.
 13. The base station of claim 12, wherein the mobile deviceand a second mobile device of the plurality of mobile devices arelocated within a predetermined distance from each other, and wherein theoperations further comprise selecting the mobile device according to arandom selection between the mobile device and the second mobile device.14. The base station of claim 10, wherein the signals are detected overa communication channel, and wherein the operations further comprisereceiving, from the mobile device, a severity analysis of theinterference with respect to the communication channel, comprising a biterror rate of the communication channel, a packet loss rate of thecommunication channel, traffic information for the communicationchannel, or any combination thereof.
 15. The base station of claim 10,wherein the data regarding the source of the interference furthercomprises a strength of the interference, a time of occurrence of theinterference, a frequency of occurrence of the interference, or anycombination thereof, and wherein the time of occurrence of theinterference or the frequency of occurrence of the interference arebased on a time stamp from the mobile device.
 16. The base station ofclaim 10, wherein the base station comprises a mobile device incommunication with the diagnostic system, and wherein the data furthercomprises a time of occurrence of the interference, a frequency ofoccurrence of the interference, or any combination thereof.
 17. Amethod, comprising: selecting, by a system comprising a processor, a setof mobile devices from among a plurality of mobile devices; sending, bythe system, a request to the set of mobile devices to perform a spectralanalysis of signals; receiving, by the system, data provided by the setof mobile devices in response to the request and in accordance with thespectral analysis performed by the set of mobile devices, wherein thedata comprises location data and spectral data; determining, by thesystem, a location of a source of interference from the location data;accessing, by the system, a database of interference sources includingspectral profiles; and identifying, by the system, the source of theinterference based on a comparison of the spectral data and the spectralprofiles.
 18. The method of claim 17, comprising generating, by thesystem, a report including information regarding the interference,wherein the report is provided to a diagnostic system to mitigateeffects of the interference, wherein the selecting further comprisesusing a random selection procedure to select a mobile device from pluralmobile devices located within a predetermined distance from each other.19. The method of claim 18, wherein the system comprises a mobile devicein communication with the diagnostic system, and wherein the datafurther comprises a time of occurrence of the interference, a frequencyof occurrence of the interference, or any combination thereof.
 20. Themethod of claim 17, wherein the signals are detected over acommunication channel, and further comprising receiving, by the system,a severity analysis of the interference performed by the set of mobiledevices with respect to the communication channel, the severity analysiscomprising a bit error rate of the communication channel, a packet lossrate of the communication channel, traffic information for thecommunication channel, or any combination thereof.